Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method comprising: determining, by a processor deployed in a network function virtualization infrastructure, an amount of resources consumed by a virtual network function subsequent to a scaling of the amount of the resources in response to an occurrence of a predefined trigger event; determining, by the processor, an amount of time elapsed between a detection of the predefined trigger event and a completion of the scaling; determining, by the processor, a value of a key performance indicator for the virtual network function subsequent to the completion of the scaling; evaluating, by the processor, an efficiency of the predefined trigger event that triggers the scaling, based on the amount of the resources consumed by the virtual network function subsequent to the scaling, the amount of time elapsed between the detection of the predefined trigger event and the completion of the scaling, and the key performance indicator for the virtual network function subsequent to the completion of the scaling; and adjusting, by the processor, the predefined trigger event based on the evaluating.
In the domain of network function virtualization (NFV), this invention addresses the challenge of optimizing resource scaling for virtual network functions (VNFs) to improve efficiency and performance. The method involves monitoring and evaluating the effectiveness of predefined trigger events that initiate scaling operations in an NFV infrastructure. After a scaling event occurs in response to a trigger, the system measures the resources consumed by the VNF, the time taken to complete the scaling, and the resulting key performance indicators (KPIs) of the VNF. These metrics are used to assess the efficiency of the trigger event, determining whether it led to optimal resource utilization, timely scaling, and improved performance. Based on this evaluation, the system adjusts the trigger event parameters to enhance future scaling decisions. This approach ensures that scaling operations are both responsive and resource-efficient, adapting dynamically to changing network conditions. The method leverages real-time data to refine trigger conditions, reducing unnecessary scaling and improving overall system performance.
2. The method of claim 1 , wherein the predefined trigger event caused the scaling to occur in response to a traffic load processed by the virtual network function exceeding a predefined threshold.
A method for dynamically scaling virtual network functions (VNFs) in a telecommunications network addresses the challenge of efficiently managing network resources under varying traffic conditions. The method involves monitoring traffic load processed by a VNF and triggering automatic scaling when the load exceeds a predefined threshold. This ensures optimal resource allocation, preventing performance degradation during peak traffic while avoiding unnecessary resource consumption during low-traffic periods. The scaling process adjusts the number of VNF instances or their computational resources based on real-time traffic demands, maintaining service quality and cost efficiency. The predefined threshold is set based on historical traffic patterns or service-level agreements to determine when scaling is necessary. This approach enhances network flexibility and responsiveness, particularly in cloud-based or virtualized environments where dynamic resource allocation is critical. The method supports both horizontal scaling (adding or removing VNF instances) and vertical scaling (adjusting resource allocation per instance) to adapt to fluctuating workloads. By automating the scaling process, the solution reduces manual intervention, minimizes downtime, and improves overall network efficiency.
3. The method of claim 2 , wherein the traffic load comprises a number of concurrent sessions being processed by the virtual network function.
A system and method for managing traffic load in virtual network functions (VNFs) involves monitoring and dynamically adjusting the processing of concurrent sessions to optimize performance. The invention addresses the challenge of efficiently handling varying traffic demands in virtualized network environments, where traditional static resource allocation can lead to inefficiencies or bottlenecks. The method includes determining the traffic load based on the number of concurrent sessions being processed by the VNF, which allows for real-time assessment of system utilization. By analyzing this load, the system can dynamically allocate or reallocate resources, such as compute, memory, or network bandwidth, to ensure smooth operation under fluctuating conditions. This adaptive approach prevents overloading individual VNF instances while maintaining service quality. The invention also supports scaling operations, such as adding or removing VNF instances, based on the observed traffic load to further enhance system responsiveness. The solution is particularly useful in cloud-based or software-defined networking (SDN) environments where flexibility and scalability are critical. By continuously monitoring and adjusting to the number of concurrent sessions, the system ensures efficient resource utilization and reliable service delivery.
4. The method of claim 2 , wherein the traffic load comprises a number of packets per second being processed by the virtual network function.
A system and method for managing traffic load in virtual network functions (VNFs) involves monitoring and dynamically adjusting the processing of network packets to optimize performance. The invention addresses the challenge of efficiently handling varying traffic loads in virtualized network environments, where traditional hardware-based solutions may lack flexibility. The method includes determining the traffic load experienced by a VNF, which is defined by the number of packets processed per second. By analyzing this metric, the system can identify periods of high or low traffic and adjust the VNF's resource allocation or processing parameters accordingly. This may involve scaling computational resources, prioritizing certain traffic flows, or redistributing workloads across multiple VNF instances. The goal is to maintain stable performance, reduce latency, and prevent resource exhaustion during peak traffic conditions. The invention also includes mechanisms for real-time monitoring and adaptive control, ensuring that the VNF can respond to changing network conditions without manual intervention. This approach enhances the scalability and reliability of virtualized network functions in cloud-based or software-defined networking (SDN) environments.
5. The method of claim 1 , wherein the predefined trigger event caused the scaling to occur in response to the value of the key performance indicator falling below a predefined threshold.
This invention relates to automated scaling systems for computing resources, addressing the challenge of dynamically adjusting resource allocation based on performance metrics. The system monitors key performance indicators (KPIs) of a computing environment, such as CPU usage, memory consumption, or response latency, to determine when scaling is necessary. When a predefined trigger event occurs, such as a KPI value falling below a specified threshold, the system automatically scales the resources up or down to maintain optimal performance. The predefined threshold acts as a critical benchmark, ensuring that scaling actions are taken only when performance degrades beyond acceptable limits. The system may involve scaling compute instances, adjusting memory allocation, or modifying network bandwidth based on the detected KPI values. This approach prevents resource waste during low-demand periods while ensuring sufficient capacity during peak loads. The invention improves efficiency by automating scaling decisions, reducing manual intervention, and optimizing cost and performance in cloud or distributed computing environments.
6. The method of claim 1 , wherein the predefined trigger event caused the scaling to occur in response to the value of the key performance indicator exceeding a predefined threshold.
Automatic scaling systems adjust computational resources based on performance metrics to optimize efficiency and cost. A method for dynamic resource allocation involves monitoring key performance indicators (KPIs) of a computing system and scaling resources in response to predefined trigger events. The method includes detecting a KPI value that exceeds a predefined threshold, which then initiates an automatic scaling action. This scaling action may involve increasing or decreasing computational resources, such as virtual machines, containers, or processing power, to maintain system performance or reduce costs. The predefined threshold ensures that scaling occurs only when necessary, preventing over-provisioning or under-provisioning of resources. The method may also include logging scaling events and adjusting thresholds dynamically based on historical performance data. This approach improves system reliability, responsiveness, and cost-efficiency by ensuring resources are allocated proportionally to demand. The method is applicable in cloud computing environments, microservices architectures, and distributed systems where workloads fluctuate.
7. The method of claim 1 , wherein the predefined trigger event caused the scaling to occur in response to a usage by a virtual machine of a resource of the resources exceeding a predefined threshold.
This invention relates to cloud computing and resource management systems, specifically addressing the challenge of dynamically scaling computing resources in response to real-time usage demands. The method involves monitoring resource utilization by virtual machines (VMs) and automatically scaling resources when usage exceeds predefined thresholds. The system tracks metrics such as CPU, memory, or storage consumption and triggers scaling actions—such as adding or removing VM instances or adjusting resource allocations—when thresholds are breached. This ensures efficient resource utilization, prevents performance degradation, and optimizes costs by avoiding over-provisioning. The method may involve horizontal scaling (adding/removing VMs) or vertical scaling (modifying resource allocations for existing VMs). The predefined thresholds are configurable, allowing customization based on workload requirements. The system may also incorporate predictive analytics to anticipate usage patterns and preemptively adjust resources. This approach enhances system responsiveness, reliability, and cost-efficiency in cloud environments.
8. The method of claim 1 , wherein the predefined trigger event caused the scaling to occur in response to an internal metric of the virtual network function exceeding a predefined threshold.
A method for scaling virtual network functions (VNFs) in a cloud computing environment addresses the challenge of dynamically adjusting computational resources to meet performance demands. The method monitors internal metrics of a VNF, such as CPU usage, memory consumption, or throughput, and compares these metrics against predefined thresholds. When a metric exceeds its threshold, the system automatically triggers a scaling event, either increasing or decreasing the allocated resources to maintain optimal performance. This approach ensures efficient resource utilization while preventing service degradation due to overloading or underutilization. The scaling process may involve adding or removing virtual machines, containers, or other computational units hosting the VNF. The method is particularly useful in telecommunication networks, where VNFs handle critical functions like routing, firewalling, or load balancing, and must adapt to fluctuating traffic patterns. By automating scaling based on real-time internal metrics, the system reduces manual intervention and improves operational efficiency. The predefined thresholds can be adjusted based on historical data, predictive analytics, or operator-defined policies to optimize performance and cost. This method enhances scalability, reliability, and cost-effectiveness in cloud-based network deployments.
9. The method of claim 8 , wherein the internal metric is a queue length.
A system and method for managing network traffic flow control involves monitoring and adjusting data transmission rates based on internal metrics to optimize performance. The method addresses the challenge of efficiently distributing data across a network while preventing congestion and ensuring fair resource allocation. A key aspect is the use of an internal metric, specifically a queue length, to dynamically regulate the flow of data. The queue length represents the number of data packets waiting in a buffer, serving as an indicator of network congestion. By continuously measuring this metric, the system can detect when the queue is approaching capacity, signaling potential bottlenecks. In response, the system adjusts the transmission rate of data sources to prevent overflow and maintain stable network performance. This approach ensures that data is transmitted at a sustainable pace, reducing packet loss and latency while maximizing throughput. The method may also incorporate additional techniques, such as rate limiting or prioritization, to further enhance efficiency. The overall solution provides a proactive and adaptive mechanism for managing network traffic, improving reliability and user experience in data transmission environments.
10. The method of claim 8 , wherein the internal metric is an amount of dynamic memory not freed.
A system and method for monitoring and optimizing memory usage in a computing environment, particularly in applications where dynamic memory allocation and deallocation occur frequently. The problem addressed is inefficient memory management, where dynamically allocated memory is not properly freed, leading to memory leaks, degraded performance, and system instability. The invention provides a solution by tracking an internal metric representing the amount of dynamic memory that remains allocated but is no longer in use. This metric is continuously monitored to detect memory leaks and identify inefficient memory usage patterns. The system may include a memory monitoring module that periodically scans memory allocations, compares them against active usage, and quantifies the unused but allocated memory. The method further includes generating alerts or triggering automated cleanup processes when the amount of unfreed dynamic memory exceeds predefined thresholds. The solution helps developers and system administrators identify and resolve memory leaks, optimize memory allocation strategies, and improve overall system reliability. The approach is applicable to various software applications, including real-time systems, embedded devices, and large-scale distributed computing environments.
11. The method of claim 1 , wherein the determining the amount of the resources consumed by the virtual network function subsequent to the scaling comprises: determining, by the processor a difference between the amount of the resources consumed by the virtual network function subsequent to the scaling and an estimated resource capacity associated with the predefined trigger event.
This invention relates to resource management in virtualized network functions (VNFs), specifically addressing the challenge of accurately assessing resource consumption after scaling operations to optimize performance and efficiency. The method involves monitoring and analyzing resource usage by a VNF after it has been scaled (e.g., up or down) to ensure it aligns with expected performance thresholds. The core process includes calculating the difference between the actual resource consumption post-scaling and the estimated resource capacity tied to the predefined trigger event that initiated the scaling. This comparison helps determine whether the scaling action achieved the desired outcome, such as reducing resource waste or preventing overutilization. The method may also involve adjusting scaling parameters or triggering further actions based on the discrepancy between actual and estimated resource usage. By dynamically evaluating resource consumption post-scaling, the system ensures efficient allocation and prevents unnecessary scaling events, improving overall network performance and cost-effectiveness. The approach is particularly useful in cloud-based or virtualized environments where dynamic scaling is common but resource tracking is often reactive rather than predictive.
12. The method of claim 11 , wherein the evaluating indicates that the amount of resources consumed by the virtual network function exceeds the estimated resource capacity associated with the predefined trigger event.
A system and method for managing virtual network functions (VNFs) in a cloud computing environment addresses the challenge of efficiently allocating and monitoring computational resources to ensure optimal performance and cost-effectiveness. The invention provides a mechanism to evaluate resource consumption of VNFs against predefined thresholds to detect potential overutilization or inefficiencies. When a VNF's resource usage exceeds an estimated capacity associated with a predefined trigger event, such as a performance degradation or cost threshold, the system initiates corrective actions. These actions may include scaling resources, migrating the VNF to a different host, or adjusting configurations to maintain service quality while minimizing operational costs. The method involves continuous monitoring of resource metrics, such as CPU, memory, and network bandwidth, and comparing them against predefined thresholds to determine whether the VNF is operating within acceptable limits. If the evaluation indicates that resource consumption exceeds the estimated capacity, the system triggers automated responses to mitigate the issue, ensuring efficient resource utilization and preventing service disruptions. This approach enhances the scalability and reliability of cloud-based network functions by dynamically adapting to changing workload demands.
13. The method of claim 12 , wherein the adjusting comprises: increasing, by the processor, the estimated resource capacity associated with the predefined trigger event.
This invention relates to resource management in computing systems, specifically adjusting estimated resource capacity in response to predefined trigger events. The problem addressed is the need for dynamic resource allocation to optimize system performance and efficiency. The method involves monitoring system conditions and adjusting resource capacity estimates when specific trigger events occur. The adjustment process includes increasing the estimated resource capacity in response to a predefined trigger event, ensuring that the system can handle increased demand without performance degradation. The method may also involve determining the type of trigger event, identifying the associated resource, and calculating the adjustment factor based on historical data or predefined rules. The system dynamically updates resource allocation to maintain optimal performance, particularly in environments with variable workloads or unpredictable demand patterns. The invention aims to improve resource utilization, reduce bottlenecks, and enhance overall system efficiency by proactively adjusting capacity estimates.
14. The method of claim 1 , wherein the evaluating indicates that the amount of time elapsed between the detection of the predefined event and the completion of the scaling exceeds a predefined threshold.
A system and method for monitoring and evaluating the performance of automated scaling processes in computing environments. The invention addresses the problem of inefficient or delayed scaling operations, which can lead to resource waste or performance degradation in cloud-based or distributed systems. The method involves detecting a predefined event that triggers a scaling operation, such as an increase or decrease in computational resources. The system then measures the time elapsed between the detection of this event and the completion of the scaling operation. If the elapsed time exceeds a predefined threshold, the system evaluates the scaling process as suboptimal or faulty. This evaluation can be used to trigger corrective actions, such as adjusting scaling parameters, alerting administrators, or initiating diagnostic procedures. The predefined event may include metrics like CPU usage, memory consumption, or network traffic, and the scaling operation may involve adding or removing virtual machines, containers, or other computational resources. The predefined threshold is set based on system requirements, ensuring that scaling operations meet performance expectations. The invention improves system efficiency by identifying and addressing delays in scaling operations, thereby optimizing resource utilization and maintaining system performance.
15. The method of claim 14 , wherein the adjusting comprises: adjusting, by the processor, a predefined threshold for activating the predefined trigger event to cause the predefined trigger event to activate sooner.
This invention relates to systems for adjusting trigger events in automated processes, particularly in environments where real-time responsiveness is critical. The problem addressed is the need to dynamically modify trigger conditions to improve system performance or user experience by activating predefined actions at more optimal times. The method involves a processor that monitors system conditions and adjusts a predefined threshold associated with a trigger event. The adjustment causes the trigger event to activate sooner than it would under default conditions. This allows for proactive rather than reactive system behavior, which can be useful in applications such as predictive maintenance, automated alerts, or adaptive user interfaces. The adjustment may be based on historical data, real-time analytics, or user preferences, ensuring the system responds more efficiently to changing conditions. The method may also include validating the adjusted threshold to confirm it achieves the desired outcome without unintended consequences. This approach enhances system responsiveness and adaptability in dynamic environments.
16. The method of claim 1 , wherein the evaluating indicates that the value of the key performance indicator is outside a predefined range.
A system and method for monitoring and evaluating key performance indicators (KPIs) in industrial or operational environments. The invention addresses the challenge of detecting deviations in KPI values that may indicate inefficiencies, faults, or suboptimal performance in processes or systems. The method involves continuously or periodically measuring a KPI, such as production rate, energy consumption, or system efficiency, and comparing its value against predefined thresholds or ranges. If the KPI value falls outside the acceptable range, the system generates an alert or triggers a corrective action. The predefined range may be dynamically adjusted based on historical data, environmental conditions, or operational parameters to improve accuracy. The system may also log the deviation for further analysis, enabling root cause identification and process optimization. The method ensures timely detection of performance issues, reducing downtime and improving overall system reliability. The invention is applicable in manufacturing, energy management, and other industries where real-time monitoring of KPIs is critical.
17. The method of claim 16 , wherein the adjusting comprises: adjusting, by the processor, a predefined threshold for activating the predefined trigger event to cause the predefined trigger event to activate sooner.
This invention relates to systems for adjusting trigger events in automated processes, particularly in environments where real-time responsiveness is critical. The problem addressed is the need to dynamically modify trigger conditions to ensure timely activation of predefined events, such as alerts or actions, without manual intervention. The method involves a processor that monitors system conditions and adjusts a predefined threshold for activating a trigger event. The adjustment is made to cause the trigger event to activate sooner than it would under the original threshold. This ensures that the system responds more quickly to changing conditions, improving efficiency and reducing delays. The adjustment may be based on historical data, predictive analytics, or real-time feedback to optimize the timing of the trigger event. The method may also include additional steps such as analyzing system performance metrics, comparing current conditions to past thresholds, and dynamically recalibrating the threshold to maintain optimal responsiveness. This dynamic adjustment prevents missed opportunities or delayed reactions, which is particularly useful in applications like industrial automation, financial trading, or cybersecurity, where timing is critical. The invention ensures that trigger events are activated at the most effective moment, enhancing system reliability and performance. By automating the adjustment process, it reduces the need for manual oversight and improves overall system adaptability.
18. The method of claim 1 , wherein the adjusting targets a value of an efficiency measure that falls within a predefined range.
This invention relates to optimizing efficiency in a system by adjusting targets to ensure an efficiency measure remains within a predefined range. The method involves monitoring an efficiency measure, such as energy efficiency or operational performance, and dynamically adjusting system parameters to maintain the measure within acceptable limits. The predefined range ensures the system operates optimally without exceeding efficiency thresholds, which could lead to inefficiencies or failures. The adjustment process may involve modifying control settings, resource allocation, or operational parameters based on real-time or historical data. The method is applicable to various systems, including industrial processes, energy management systems, and computational workloads, where maintaining efficiency within a specific range is critical. By dynamically targeting the efficiency measure, the system avoids underperformance or excessive resource consumption, ensuring reliable and cost-effective operation. The predefined range accounts for variability in operating conditions, allowing the system to adapt while staying within optimal efficiency boundaries. This approach enhances overall system performance and sustainability by preventing deviations that could compromise efficiency or lead to inefficiencies.
19. A non-transitory computer-readable medium storing instructions which, when executed by a processor deployed in a network function virtualization infrastructure, cause the processor to perform operations, the operations comprising: determining an amount of resources consumed by a virtual network function subsequent to a scaling of the amount of the resources in response to an occurrence of a predefined trigger event; determining an amount of time elapsed between a detection of the predefined trigger event and a completion of the scaling; determining a value of a key performance indicator for the virtual network function subsequent to the completion of the scaling; evaluating an efficiency of the predefined trigger event that triggers the scaling, based on the amount of the resources consumed by the virtual network function subsequent to the scaling, the amount of time elapsed between the detection of the predefined trigger event and the completion of the scaling, and the key performance indicator for the virtual network function subsequent to the completion of the scaling; and adjusting the predefined trigger event based on the evaluating.
This invention relates to optimizing resource scaling in a network function virtualization (NFV) infrastructure. The problem addressed is the inefficiency in dynamically scaling virtual network functions (VNFs) in response to predefined trigger events, which may lead to suboptimal resource utilization, performance degradation, or unnecessary scaling delays. The system monitors a VNF after scaling occurs in response to a trigger event. It measures the resources consumed by the VNF post-scaling, the time taken from trigger detection to scaling completion, and the VNF's key performance indicator (KPI) after scaling. These metrics are used to evaluate the efficiency of the trigger event in achieving desired performance while minimizing resource waste and latency. Based on this evaluation, the system adjusts the trigger event parameters to improve future scaling decisions. The adjustment may involve modifying thresholds, timing, or conditions under which scaling is initiated. The goal is to refine the scaling process to balance performance, resource usage, and responsiveness in NFV environments.
20. A system comprising: a processor deployed in a network function virtualization infrastructure; and a non-transitory computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations, the operations comprising: determining an amount of resources consumed by a virtual network function subsequent to a scaling of the amount of the resources in response to an occurrence of a predefined trigger event; determining an amount of time elapsed between a detection of the predefined trigger event and a completion of the scaling; determining a value of a key performance indicator for the virtual network function subsequent to the completion of the scaling; evaluating an efficiency of the predefined trigger event that triggers the scaling, based on the amount of resources consumed by the virtual network function subsequent to the scaling, the amount of time elapsed between the detection of the predefined trigger event and the completion of the scaling, and the key performance indicator for the virtual network function subsequent to the completion of the scaling; and adjusting the predefined trigger event based on the evaluating.
The system operates in a network function virtualization (NFV) infrastructure to optimize resource scaling for virtual network functions (VNFs). The problem addressed is inefficient scaling decisions that may lead to resource waste or performance degradation. The system monitors resource consumption, scaling time, and key performance indicators (KPIs) after scaling events triggered by predefined conditions. By analyzing these metrics, it evaluates the effectiveness of the scaling triggers and adjusts them to improve efficiency. The processor executes instructions to track resource usage post-scaling, measure the time between trigger detection and scaling completion, and assess KPIs like latency or throughput. The system then refines the trigger criteria to balance resource utilization and performance. This adaptive approach ensures dynamic adjustments to scaling policies, enhancing overall NFV efficiency. The solution automates the optimization of scaling triggers, reducing manual intervention and improving system responsiveness to workload changes.
Unknown
November 3, 2020
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